Non-specific yet selective interactions contribute to small molecule condensate partitioning behavior

Abstract Biomolecular condensates are essential in various cellular processes, and their misregulation has been demonstrated to be underly disease. Small molecules that modulate condensate stability and material properties offer promising therapeutic approaches, but mechanistic insights into their interactions with condensates remain largely lacking. We employ a multiscale approach to enable long-time, equilibrated all-atom simulations of various condensate-ligand systems. Systematic characterization of the ligand binding poses reveals that condensates can form diverse and heterogeneous chemical environments with one or multiple chains to bind small molecules. Unlike traditional protein-ligand interactions, these chemical environments are dominated by non-specific hydrophobic interactions. Nevertheless, the chemical environments feature unique amino acid compositions and physicochemical properties that favor certain small molecules over others, resulting in varied ligand partitioning coefficients within condensates. Notably, different condensates share similar sets of chemical environments but at different populations. This population shift drives ligand selectivity towards specific condensates. Our approach can enhance the interpretation of experimental screening data and may assist in the rational design of small molecules targeting specific condensates.


Introduction
1][22][23][24][25] Developing chemical probes and therapeutics that can modify the properties of condensates offers a promising strategy for studying and potentially treating these conditions.Small molecules may help restore normal cellular functions in diseases where condensate localization, dynamics, and material properties are altered. 14,16,19,26,279][30] These studies quantify the fraction of small molecules inside the condensate versus those outside, referred to as partition coefficients, using various techniques.Several trends emerge across the library of molecules investigated.Small molecule mass spectrometry experiments have led to the discovery that lipids and small molecule drugs can concentrate in condensates, as a result of their hydrophobic character. 29,30Fluorescent probe partitioning experiments explored using microscopy has 14,28 uncovered differences in the chemical environments of condensates that could be predictive of small molecule partitioning behavior in live cells.
Insights into the atomistic interactions driving small molecule partitioning could help understand the reported results and guide the rational design of molecules that could interact with condensate.
2][33][34] However, the unique properties of condensates may result in interactions with ligands that differ from established paradigms.Notably, many proteins that form condensates are disordered, lacking the well-folded regions necessary for creating binding pockets to facilitate specific interactions. 11,15,35,36Additionally, condensates consist of multiple chains, raising the question of whether cross-chain interactions contribute to ligand binding differently than in single chains.Molecular dynamics (MD) simulations, which have been instrumental in identifying key factors for ligand binding affinity and in the rational design of small molecules targeting single proteins, 33,37,38 can also be applied to study condensate-ligand interactions.
We conduct long-timescale atomistic MD simulations to reveal the chemical environments within protein condensates that facilitate the partitioning of ligand molecules.A multiscale approach was used to equilibrate protein-protein contacts in three models of cellular condensates modeled by homotypic protein condensates: transcriptional (MED1), granular cluster of the nucleolus (NPM1), and constitutive heterochromatin (HP1α), which have been previously characterized experimentally. 19We introduce protein-ligand interaction fingerprints to systematically characterize the variety of binding poses observed in the atomistic simulations.Low-dimensional embedding of these interaction fingerprints revealed multiple clusters of chemical environments for ligand binding.These environments feature nonspecific interactions, often enriched with hydrophobic residues from both the same and different protein chains, highlighting the significant role of ligand hydrophobicity in determining partition coefficients.Notably, the simulations identified specific chemical groups in ligand molecules that contribute to these interactions.Ligands enriched with these groups were found to partition more effectively in protein condensates compared to those without them.Microenvironments with similar chemical compositions were found across the three protein condensates, indicating common features.However, significant shifts in the population of these chemical environments were observed, contributing to the selectivity of ligands targeting specific condensates.
3][54] As described in the Methods section and illustrated in Figure 1A, this approach involves three steps that gradually increase the model resolution.We began with an α-carbon-only protein model, MOFF, 40,55 that facilitates large-scale conformational changes.
Next, we transitioned to an explicit solvent coarse-grained model with the MARTINI force fields to relax the solvation environment around proteins. 56Finally, we employed an explicit solvent all-atom model with the CHARMM36m force field 57 for accurate descriptions of protein-ligand interactions.
We applied the multiscale approach to construct all-atom models for three condensates that have been extensively characterized in prior experiments: HP1α, MED1, and NPM1.

Simulations at different resolutions produced consistent interchain contact patterns among various amino acids (Figures 1B and S1-S3
). R 2 values between the MOFF or MARTINI and all-atom simulation results are above 0.75 for all proteins.We also repeated the simulation protocol for HP1α three times, each time selecting different configurations produced by MOFF simulations to initialize the higher-resolution simulations.The resulting three allatom simulations yielded similar statistics for amino acid contacts (Figure S4).These results support the multiscale approach's ability in producing equilibrated, atomistic condensate organizations.

Interaction fingerprints reveal the chemical and dynamical nature of condensate-ligand interactions
Using the atomistic configurations derived from multiscale simulations, we conducted additional two-microsecond explicit solvent atomistic MD simulations to investigate the interactions between protein condensates and small molecules.We focused on three types of small molecules: Mitoxantrone, Daunorubicin, and Proflavine, which have been shown to differentially partition in the three condensate systems. 28For HP1α condensates, we also simulated a more hydrophilic molecule, Citicoline.A total of 10 simulations were performed, each corresponding to a specific condensate-ligand combination, resulting in a cumulative simulation time of 20 microseconds.
The condensate systems consist of numerous protein copies, creating a wide variety of local chemical environments.To comprehensively characterize all potential chemical environments that could accommodate small molecules, we introduced 80 copies of each small molecule into the condensate system for simulations.The presence of multiple small molecules enabled the exploration of potential binding poses in parallel, rather than relying on the slower diffusion process of individual small molecules.
9][60][61][62] As detailed in the Methods section and illustrated in Fig- ure 2A, these fingerprints represent each binding pose with a binary vector that annotates the sets of amino acids contacting the ligand molecule and the types of interactions formed.
Consequently, the fingerprints capture both structural and chemical information of the binding pose.To simplify fingerprint representation, we consolidated amino acids from different chains.When preserving the residue index of each amino acid during this consolidation, we refer to the resulting vector as index-based fingerprints.However, unless otherwise specified, we further group amino acids of the same type together without preserving the residue indices to facilitate transferable analyses across proteins.The statistics of interaction fingerprints revealed the diversity of small molecule binding poses.For example, we calculated the probability distribution of the total number of interactions, determined as a direct sum of the fingerprint vector, formed between each Mitoxantrone molecule and the HP1α condensate.As shown in Figure 2B, the number of interactions in the binding poses varies widely.The average number of amino acids involved in ligand binding is approximately five (Figure S6).This diversity in binding poses supports the non-specific nature of condensate-ligand interactions.Analyses of other systems yield similar results (Figures S5 and S6).Furthermore, the statistical similarity between the first and second halves of the trajectories supports the convergence of the simulations (Figures S7 and S8).
To elucidate the dynamic nature of condensate-ligand interactions, we computed the similarity of index-based fingerprints for each ligand molecule at different simulation times (Eq. 1 in the Methods section).The similarity values range from 0, indicating no overlap between two binding poses, to 1, indicating identical binding poses (see Methods for details).
The similarity values along a simulation trajectory can be represented as a matrix, providing a visual representation of ligand dynamics (Figures 2 and S9).When the ligand interacts with multiple distinct binding sites, high similarity values appear during the time it occupies each site, forming a domain along the diagonal in the corresponding matrix (Figure 2C, left).
Off-diagonal elements, representing similarity values for fingerprints in different binding sites, are low.In contrast, if a ligand remains stably bound to a single site, the similarity matrix displays high values throughout (Figure 2C, right).
The average of the similarity matrix indicates the number of binding poses explored by the ligand, particularly when compared with theoretical values estimated for ligands binding at exactly n sites during the simulation (see Supporting Information for details).As shown in Figure 2D, for most ligands, the similarity matrix average is lower than the theoretical value calculated for n = 10.This suggests that ligand molecules dynamically explore numerous distinct binding environments within the condensate.Similar conclusions can be drawn from analyses of other condensate-ligand systems (Figure S10).

Chemical environments are created from single and multichain interactions
While protein-ligand interactions have been extensively studied, 31-34 condensates, which are assemblies of interacting proteins and nucleic acids, exhibit emergent properties distinct from a single biopolymer.Understanding the role of cross-chain contacts in small molecule partitioning could be crucial for differentiating condensate-drug interactions from those occurring in single proteins.We again focus our analysis on the HP1α condensate, with results for other systems provided in the Supporting Information (Figures S11-S13).
To determine whether small molecules interact with a single or multiple chains during binding, we introduced the concept of single-chain dominance (f SCD , Eq. 2 in the Methods section).This metric quantifies the maximum fraction of interactions in a binding pose originating from a single chain.By definition, f SCD ranges from 0 to 1, reaching 1 if the small molecule interacts exclusively with one protein chain.We further determined the probability distribution of f SCD using binding poses from simulations.As shown in Figure 3A, a significant fraction of the binding poses have a non-unity f SCD , indicating the involvement of multiple protein chains in small molecule interactions.
The presence of both single and multiple chain environments prompted us to examine the types of interactions they offer for ligand binding.We performed Uniform Manifold Approximation and Projection (UMAP) embedding analysis to project the high-dimensional interaction fingerprints onto two variables, UMAP1 and UMAP2.As detailed in the Methods section, UMAP is a dimension reduction technique that finds a low-dimensional projection of the data that best preserves their topological structure. 63The UMAP embeddings succeeded in partitioning the interaction fingerprints into several clusters (Figure 3D).We observed a significant overlap between the fingerprints from a single protein and those involving multiple chains.This observation is robust across different small molecules and condensates (Figure Intrachain contacts  S12).Thus, while amino acids from different protein chains can come together to bind small molecules, the chemical environments they offer are similar to those found in individual proteins.
The similarity in binding environments can be inferred from the contact maps between amino acids from the same and different chains (Figure 3B).The two maps show a significant correlation, with a R 2 value of 0.82 (Figure 3C).Consequently, the scaffold for condensateligand binding shares a similar amino acid composition, regardless of whether it is formed by a single protein chain or multiple chains, resulting in comparable chemical environments and interaction fingerprints.

Multiple chemical environments contribute to ligand partitioning
The interaction fingerprints enable a systematic analysis of the chemical nature of the binding between small molecules and condensates.To this end, we computed the fraction of different types of interactions detected.As shown in Figures 4A and S14, non-specific interactions, such as hydrophobic interactions and van der Waals contacts, are important for interactions between the ligands and proteins considered.Furthermore, as the ligands become more hydrophobic, as measured by their log P (the logarithm of the partition coefficient of a molecule between octanol and water), the fraction of hydrophobic interactions increases.
To further distinguish among various small molecules, we utilized UMAP to project their interaction fingerprints detected in the HP1α condensate onto two dimensions (Figure 4B and S15).Similar results for other condensate-ligand systems are provided in Figures S16 and   S17.The embeddings segregate the fingerprints into minimally overlapping clusters, each containing unique combinations of amino acids for ligand binding.Figure 4C demonstrates that while clusters 1 and 4 are enriched with either hydrophilic or hydrophobic residues, clusters 2 and 3 comprise a mixture of both residue types.
We found that all four molecules are present in most clusters, albeit in varying proportions.The lack of clear separation among the small molecules aligns with the dominance of non-specific interactions provided by condensates for binding.The shift in the population of binding environments is most evident in the separate scatter plots for each drug, with the fingerprints colored by the fraction of hydrophobic interactions (Figure 4D).Most fingerprints for the hydrophilic molecule Citicoline are located around cluster 1.In contrast, fingerprints for the hydrophobic molecule Proflavine are concentrated around cluster 4.
Thus, condensates offer heterogeneous chemical environments for non-specific ligand binding.Depending on their hydrophobicity, the small molecules preferentially engage with subsets of chemical environments that provide favorable interactions.The importance of drug hydrophobicity in condensate binding is consistent with previous findings on its role in predicting partitioning coefficients. 29,64iticoline log P = -4.899Simulations identify small molecule fragments contributing to condensate partitioning In addition to the condensate chemical environments, the atomistic simulations also revealed the chemical groups of small molecules that interact with amino acids forming such environments (Figure 5A).While the chemical properties of these fragments varied among the small molecules, they shared some common moieties (e.g., amines, π-systems, and carbonyl groups) that are expected to drive noncovalent interactions with proteins (Figure 5A).These fragments provide direct predictions that can be tested against experimental screening data: small molecules enriched in these moieties are expected to partition more effectively in condensates than those lacking them.
Analysis of the screening data in Kilgore et al. 28 revealed that several interaction fragments derived from Mitoxantrone, Daunorubicin, and Proflavine were identifiable in certain chemical probes (Figure 5B).Comparing the partition ratios of probes containing one of these interaction fragments (+) for specific condensates (MED1, NPM1, HP1α) with those lacking the fragment showed that partitioning behavior was influenced by its presence (Figure 5B).
For instance, chiral secondary alcohols were equally incorporated and weakly associated with partitioning.Secondary amines adjacent to a π-system had minimal impact on the partition ratio in MED1 condensates but were more influential in NPM1 and HP1α condensates.
Aromatic secondary amines were less likely to concentrate in MED1 condensates but had a significant impact in NPM1 and HP1α condensates.These results further support the idea that chemical environments in condensates enable selective small molecule partitioning and MD simulation can help guide the discovery of molecules that favor them.

Population shift in chemical environments contributes to differential ligand partitioning
Having revealed the heterogeneous chemical environments individual condensates harbor to accommodate small molecules, we next examined the distinctions of these interactions across different condensates for the same molecule.
We applied UMAP to analyze interaction fingerprints formed between Mitoxantrone and the three different protein condensates.Similar to those presented in Figure 4, the embeddings successfully identified the heterogeneous environments within condensates, with visible clusters of interaction fingerprints shown in Figure 6A.Notably, the scatter plots of the binding modes from the three condensates exhibit significant overlap.Analyses of the other two molecules produced similar results (Figures S18 and S19).Thus, small molecules can concentrate in similar chemical environments found in different condensates.The amino acid composition in different clusters is highly conserved across the three condensates (Figure 6B).
Although the interaction fingerprints from various condensates exhibit considerable over-lap, their relative distribution within the embedding space differs markedly.To examine these quantitative differences, we plotted the probability densities of interaction fingerprints in Figure 6C.The peaks of the distributions (yellow) are located at distinct positions.Furthermore, we found that the frequency of amino acids in the binding poses correlates with the frequency computed directly from the protein sequence (Figure S20).Therefore, while condensates share similar chemical environments to accommodate small molecules, they finetune the relative population with amino acid composition.
The above observations enhance our understanding of experimental data regarding the correlation of small molecule partitioning coefficients in different protein condensates.Given the similar chemical environments of various protein condensates, small molecules that are favored in one are likely to partition in others.However, variations in the relative distribution of chemical environments lead to quantitative differences in partition coefficients across condensates.

Conclusions and Discussion
In this study, we employed a multiscale simulation approach to investigate condensate-ligand interactions at atomic resolution.We also introduced a quantitative metric, the interaction fingerprint, to systematically characterize the chemical and structural features of the chemical environment for ligand binding.Analysis of these fingerprints revealed that different condensates utilize remarkably similar chemical environments for ligand binding, explaining the correlation of small molecule partitioning coefficients across condensates.However, these microenvironments are quite heterogeneous, comprising diverse binding pockets formed by distinct amino acid groups.Variations in the distribution of these pockets contribute to condensates' preference for specific ligand molecules, resulting in the observed ligand selectivity.
The similarity in chemical environments across condensates arises from the shared driving forces that contribute to condensate organization.][8][9][10]65 These interactions promote interchain contacts with similar amino acid compositions across condensates.Since small molecules only passively interact with the preformed protein-interaction network by different chains, they encounter similar chemical environments for binding.

Multiscale simulation of condensates
We employed a multiscale approach to prepare equilibrated configurations of protein condensates to investigate ligand binding environments.Initially, we used coarse-grained simulations with MOFF, a one-bead-per-amino-acid force field, which balances interactions for both folded and disordered proteins. 40Simulations were initialized by randomly placing protein molecules, 10 for HP1α dimers, 14 for NPM1, and 4 for MED1, in a cubic box of 100 nm × 100 nm × 100 nm.The process began with energy minimization followed by 0.1 µs constant pressure and constant temperature (NPT) simulations at 150 K and 1 bar, using a 10 fs timestep.This resulted in the compression of the simulation boxes to a final size of of 15 × 15 × 15 nm 3 .The box size was chosen to achieve a protein mass density of approximately 130 mg/mL, consistent with previous studies of protein condensates. 40,43,44,66Subsequent energy minimizations were followed by 0.1 µs constant volume and constant temperature (NVT) simulations, with a 10 fs timestep.During the NVT simulation, the temperature was gradually increased from 150 K to 300 K using the GROMACS 67 simulated annealing protocol.Finally, NVT simulations were conducted at 300 K for 2 µs with a 10 fs timestep.
Following these simulations, we used the MARTINI force field, 68 which includes explicit representations for water molecules and ions, and higher resolution representations for proteins based on a four heavy atoms to one coarse-grain bead mapping strategy.The final frames from the MOFF simulations were converted to atomic structures using REMO, 69 and then coarse-grained using Martinize2. 70The MARTINI protein condensates were solvated with coarse-grained water molecules and NaCl ions at a concentration of 150 mM, achieving a box size of 15 × 15 × 15 nm 3 .MARTINI simulations began with energy minimization, followed by NVT equilibration at 313.15 K for 5 ns with a 10 fs timestep, and NPT equilibration at 313.15 K and 1 bar for 10 ns with a 20 fs timestep.NPT production simulations were then conducted at 313.15 K and 1 bar for 0.6 µs with a 20 fs timestep.
Condensate structures from the final frames of the MARTINI simulations were converted to atomic structures using the Backward tool 71 for simulations with the CHARMM36m force field.The protein condensates were solvated with CHARMM-modified TIP3P water and 150 mM NaCl to achieve a box size of 15 × 15 × 15 nm 3 .Before introducing drug molecules, we equilibrated the protein-only system with a 125 ps NVT simulation at 313.15 K using a 2 fs timestep, followed by a 125 ps NPT simulation at 313.15 K and 1 bar with a 2 fs timestep.
We then conducted a 500 ns NPT simulation at 313.15 K and 1 bar with a 4 fs timestep.
After equilibration, 80 small-molecule drugs were inserted into the system by randomly replacing solvent molecules using the GROMACS insert-molecules tool, achieving a concentration of approximately 40 mM.Force fields for these molecules were generated using CGenFF. 72,73We performed 2 µs NPT simulations at 313.15 K and 1 bar with a 4 fs timestep to probe protein-ligand interactions.For all atomistic simulations with a 4 fs timestep, bond constraints were applied using the LINCS algorithm, 74 and the hydrogen mass repartitioning method 75 was used.
All simulations were performed using GROMACS, 67 with additional simulation details provided in the Supporting Information.System visualization and analysis were conducted with Mol * , 76 MDAnalysis, 77 and MDTraj. 78

Construction of condensate-ligand interaction fingerprints
We utilize the Structural Interaction Fingerprint (SIFt) methodology as outlined in previous studies 58 to characterize condensate-ligand interactions via the Python library Pro-LIF. 62Initially, we construct an interaction matrix M with dimensions N × L for a given condensate-ligand conformation.Here, N = 6 represents the number of interaction types considered, which include Hydrophobic, HBDonor, HBAcceptor, PiCation, PiStacking, and VdWContact.The parameter L = n × l where l denotes the number of amino acids in a protein chain, and n indicates the number of proteins in the condensate.Each element M i,j in the matrix is binary (1 or 0), indicating the presence or absence of the i-th interaction type between the ligand and the j-th residue.
We reduce the dimensionality of the interaction matrix by summing the columns corresponding to different protein chains but sharing the same sequence location, producing a matrix M index with dimensions 6 × l.Additionally, we define another matrix, M AA , with dimensions 6 × 20 by merging all columns of the same amino acid type and applying the same summation rule.
The interaction fingerprints are defined by flattening the matrices M index and M AA into vectors.These vectors are then binarized, resulting in index-based or residue type-based interaction fingerprints, respectively.Detailed information on the construction of these fingerprints is provided in the Supporting Information.

Calculation of similarity matrices based on index-based fingerprints
We defined the similarity between two interaction fingerprints, fp i and fp j , as s i,j = all bits (fp i ∧ fp j ) all bits (fp i ∨ fp j ) .
The operators ∧ and ∨ represent the bitwise AND and OR operations between binary fingerprints, respectively.The above definition is often referred as Tanimoto similarity for binary vectors.For each ligand molecule in a simulation system, we compute a similarity matrix in which each element represents the similarity between condensate-ligand conformations at two distinct simulation snapshots.

Definition of single-chain dominance
For a particular binding pose, the single-chain dominance, f SCD , is computed from the interaction fingerprint as where m is the number of protein chains interacting with the small molecule, and N i is the number of interactions between the small molecule and the i-th chain.

UMAP analysis on residue type-based fingerprints
We employed UMAP for dimensionality reduction of interaction fingerprints.Specifically, dimensionality reduction was performed on amino acid type-based interaction fingerprints using Python library umap-learn. 63The Jaccard distance, calculated as 1 minus the Tanimoto similarity, was used to quantify the distance between pairs of interaction fingerprints.
Default values were maintained for the remaining UMAP hyperparameters.

Identification of small molecule interaction fragments
For a frame collected from each simulation, the location on a small molecule drug and the amino acid residues it interacted with in that frame was binned and counted.The sites contacted by protein molecules on a small molecule were summed and histograms were computed to identify locations with the highest probability of interacting with MED1, HP1α, or NPM1 proteins in each simulation yielding atoms central to each contact.These atoms could then be used to a chemical environment using the Chem.FindAtomEnvironmentOfRadiusN function implemented in RDKit 79 a radius of 2 around each atom site, to construct the fragments shown in Figure 5A.These contact sites could then be used to perform a substructure search using the HasSubstructMatch as implemented in RDKit in order to identify chemical probes present in Kilgore et al.

Figure 1 :
Figure 1: Multiscale approach enables explicit solvent all-atom simulations of biomolecular condensates with equilibrated configurations.(A) Illustration of the multiscale simulation protocol that gradually increases the model resolution.(B) Contact probability maps for amino acids from different protein chains, computed from simulations at various resolutions, exhibit consistent patterns.

Figure 2 :
Figure 2: Chemical interaction fingerprints facilitate the analysis of condensateligand interactions.(A) The diagram illustrates the definition of the interaction fingerprint, which characterizes interactions between a small molecule and the set of contacting amino acids.Two types of fingerprints can be distinguished based on whether residue indices or types are preserved when consolidating amino acids from different chains.(B) Probability distribution of the total number of interactions detected in all binding poses uncovered from simulations of the HP1α condensate with Mitoxantrone.Representative configurations depicting condensate-ligand binding with a small and large number of interactions are shown on the side.(C) Representative similarity matrices of fingerprints along the simulation trajectory, corresponding to a dynamic ligand traversing through multiple binding sites (left) and a stably bound ligand (right).(D) Probability distribution of the average of similarity matrices for different Mitoxantrone molecules.The vertical lines denote theoretical lower limits of this average for a ligand exploring n unique binding poses.

Figure 3 :
Figure 3: Amino acids from single or multiple protein chains form scaffolds with similar chemical environments for ligand binding.Results are obtained from simulations of the HP1α-Mitoxantrone system.(A) Probability distribution of the single chain dominance.(B) Contact maps between amino acids from different (left) and the same (right) chain.(C) Correlation between the intra-and inter-chain amino acid contacts.The red line represents a linear fit to the data.(D) Scatter plot of the interaction fingerprints on the two dimensional UMAP embeddings.Binding poses with single and multiple chains are shown in pink and blue, respectively.

Figure 4 :
Figure 4: Condensates provide heterogeneous chemical environments for nonspecific ligand binding.Results from the HP1α condensate are shown as examples.(A) Frequency of various interactions detected in the binding pose between HP1α and different small molecules.(B) Scatter plot of the interaction fingerprints for the four small molecules in the HP1α condensate on the two dimensional UMAP embeddings.(C) Amino acid frequency determined from the interaction fingerprints that fall into the four regions with distinct UMAP embeddings shown in part B. (D) Scatter plot of the same UMAP embedding as in part B but shown separately for different small molecules.Interaction fingerprints are color-coded according to their fraction of hydrophobic interactions.

Figure 5 :
Figure 5: MD simulations identify small molecule interaction fragments that facilitate condensate partitioning.(A) Interacting fragments of small molecules identified from atomistic simulations.(B) Experimental partitioning ratios of drugs with/without fragments in panel A.

Figure 6 :
Figure 6: Mitoxantrone identifies similar chemical environments for contact across three protein condensates.(A) Scatter plot of the interaction fingerprints for Mitoxantrone in the three condensate.(B) Amino acid frequency determined from the interaction fingerprints that fall into the four regions with distinct UMAP embeddings shown in part A. (C) Probability density profile of the interaction fingerprints for Mitoxantrone in the three condensate.